This letter develops an efficient self-optimizing algorithm for Bluetooth Low Energy (BLE) networks in industrial IoT (IIoT) applications. The ultra-dense nature of IIoT applications implies that BLE networks are associated with a high probability of packet collisions, increasing the data collection latency. Determining the optimal packet transmission interval that minimizes the data collection latency is very challenging due to the non-linearity of this problem. We derive an analytic solution by which to realize an optimal advertising interval in a closed-form expression and prove its optimality and uniqueness. Based on a simple closed-form solution, we develop a low-complexity self-optimizing algorithm that minimizes the data collection latency. Simulation results confirm that the analytic results are accurate and that the proposed algorithm autonomously optimizes BLE networks.

Healthcare Internet of Things (H-IoT) has been rigorously extending its applications to hospitals to deliver important medical data remotely and continuously with various types of smart medical sensors. Medical data of patients should be treated with different urgency levels. Specifically, critical medical data, such as data pertaining, to heart failure or fall risk must be transmitted first with the highest priority. Thus, priority-aware scheduling for H-IoT networks is required to improve the quality of medical services. However, due to the high-dense nature of H-IoT networks, interference and packet collisions can make finding the optimal priority-aware scheduling very challenging. To tackle these practical issues, we develop a priority-aware scheduling algorithm based on a message-passing framework. With a message-passing procedure that relies on a max-product rule, the proposed algorithm simultaneously maximizes the priority metric and throughput of the network. In addition, the proposed algorithm efficiently finds the best scheduling configuration without a central coordinator by enabling low complexity and autonomous decisions of each network node. Our simulation results verify that the proposed algorithm outperforms conventional schemes in terms of priority awareness, throughput performance, and network scalability.

Joint processing coordinated multipoint transmission (JP-CoMP) has gained high attention as part of the effort to cope with the increasing levels of demand in the next-generation wireless communications systems. By clustering neighboring cells and with cooperative transmission within each cluster, JP-CoMP efficiently mitigates inter-cell interference and improves the overall system throughput. However, choosing the optimal clustering is formulated as a nonlinear mathematical problem, making it very challenging to find a practical solution. In this paper, we propose a distributed cell clustering algorithm that maximizes the overall throughput of the JP-CoMP scheme. The proposed algorithm renders the nonlinear mathematical problem of JP-CoMP clustering into an approximated linear formulation and introduces a multi-layer message-passing framework in order to find an efficient solution with a very low computational load. The main advantages of the proposed algorithm are that i) it enables distributed control among neighboring cells without the need for any central coordinators of the network; (ii) the computational load imposed on each cell is kept to a minimum; and, (iii) required message exchanges via backhaul result in only small levels of overhead on the network. The simulation results verify that the proposed algorithm finds an efficient JP-CoMP clustering that outperforms previous algorithms in terms of both the sum throughput and edge user throughput. Moreover, the convergence properties and the computational complexity of the proposed algorithm are compared with those of previous algorithms, confirming its usefulness in practical implementations.

The integration of IEEE802.11p and 5G New Radio for the backbone transmission of V2X (vehicle-to-everything) technology has served as a novel type of intelligent transportation system with an efficient low-cost and high-coverage area. This integrated scheme implements a V2X end-to-end data delivery system in a multi-layer mechanism. However, the dynamic nature of vehicular networks leads to numerous network outages and frequent network renewal requests, which considerably diminishing the overall performance. Hence, efficient routing management is required to maximize reliable communication. Optimizing routing management is categorized as a non-linear problem with a very high computational load, making a practical solution very challenging. To cope with the complicated routing problem, we develop a low-complexity distributed algorithm based on a hierarchical affinity propagation framework. The proposed algorithm exchanges small-sized messages among local vehicles to determine data routing more efficiently. The distributive optimization of the proposed algorithm requires a small computational load for each node. The main advantage of the proposed algorithm is that each vehicle autonomously determines its best routing options without the help of any network coordinators. Extensive simulation results verify that the proposed algorithm outperforms conventional algorithms in terms of vehicle mobility, network scalability, the level of network overhead, and the algorithm convergence rate.

An efficient channel assignment plays an important role in mitigating co-channel interference in ultra-dense wireless networks. A simple solution is to separate interfering network nodes into orthogonal channels to reduce the interference among them. However, determining the optimal channel assignment is considered to be a non-linear problem, which may also be associated with practical implementation issues such as high computational complexity and control signaling issues. In an effort to cope with these challenging issues, we propose a distributed channel assignment algorithm that efficiently finds the optimal channel configuration by utilizing the concept of belief propagation. Based on a message-passing framework, the proposed distributed channel assignment algorithm maximizes the overall sum rate of the ultra-dense network with a low computational load for each network node. In addition, we design a network protocol and frame format to implement the proposed message-passing framework to real-world wireless networks. The main advantage of the proposed approach is that network nodes autonomously determine the optimal channel assignment and rapidly adapt to dynamic changes of the network. Simulation results confirm that the proposed distributed channel assignment algorithm outperforms conventional algorithms in terms of various network performance aspects, such as the sum rate, scalability, latency, and user mobility.

This paper presents a comprehensive performance analysis and optimization of ultra-dense Internet-of-Things (IoT) networks. While the high density of Bluetooth Low Energy (BLE) devices in IoT networks improves wireless coverage and enhances connectivity, these coexisting devices can also lead to increased interference and collisions, degrading device discovery performance and increasing the network’s overall latency. In this work, we provide a mathematical analysis of the discovery latency in densely deployed BLE networks. Our mathematical model constructs a Markov chain model that implements the impacts of packet collisions and duplication on discovery latency. Based on a thorough performance analysis, we propose a novel duplication-avoidance protocol in which BLE devices cooperatively adjust advertising and minimize the network’s overall discovery latency. Our simulation results shows that the proposed novel duplication-avoidance protocol consistently reduces latency compared to the standard discovery protocol in densely deployed BLE networks, with relative improvements ranging from 7.63% to 8.16%.